-Unfortunately, I don't have a study with objective measurements on hand (let me know in the comments if you do!), but I think most native English speakers who try this exercise and introspect—especially using examples where the trans person exhibits features or behavior typical of their natal sex—will agree with Kerr's assessment: "You can know perfectly the actual sex of a male person, and yet you will still react differently if someone calls them _she_ instead of _he_."
+Unfortunately, I don't have a study with objective measurements on hand (let me know in the comments if you do!), but I think most native English speakers who try this exercise and introspect—especially using examples where the trans person exhibits features or behavior typical of their natal sex, with things like "she ejaculated" or "he gave birth" being the starkest examples—will agree with Kerr's assessment: "You can know perfectly the actual sex of a male person, and yet you will still react differently if someone calls them _she_ instead of _he_."
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+Let's relate this is Yudkowsky's specialty of artificial intelligence. In a post on ["Multimodal Neurons in Artificial Neural Networks"](https://openai.com/blog/multimodal-neurons/), Gabriel Goh _et al._ explore the capabilities and biases of the [CLIP](https://openai.com/blog/clip/) neural network trained on textual and image data.
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+There are some striking parallels between CLIP's behavior, and phenomena observed in neuroscience. Neurons in the human brain have been observed to respond to the same concept represented in different modalities (_e.g._, [Quiroga _et al._](/papers/quiroga_et_al-invariant_visual_representation_by_single_neurons.pdf) observed a neuron in one patient that responded to photos and sketches of actress Halle Berry, as well as the text string "Halle Berry"), and so do CLIP neurons. Futhermore, CLIP is vulnerable to a Stroop-like effect where its image-classification capabilities can be fooled by "typographic attacks"—a dog with instances of the text "$$$" superimposed over it gets classified as a piggy bank, an apple with a handwritten sign saying "LIBRARY" gets classified as a library. The network knows perfectly what dogs and apples look like, and yet still reacts differently if adjacent text calls them something else.
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+I conjecture that the appeal of subject-chosen pronouns lies _precisely_ in how they exert Stroop-like effects on speakers' and listeners' cognition. (Once again, if it were _actually true_ that _she_ and _he_ had no difference in meaning, _there would be no reason to care_.) [Pronoun badges](/2018/Oct/sticker-prices/) are, quite literally, a typographic attack against native English speakers' brains.